标题： 精确的非参数敏感性分析用于对列联表观察研究的分析 显示英文标题

标题： Exact, Nonparametric Sensitivity Analysis for Observational Studies of Contingency Tables

摘要： 在观察性研究中，列联表提供了一种简单且直观的方法来研究分类变量之间的关联。 然而，由于未测量的混杂因素，列联表中的任何关联检验都可能产生偏差。 现有的评估未测量混杂因素对关联检验影响的敏感性分析通常假设一个二元处理变量，或者对非二元处理变量施加强参数假设。 我们通过一种精确（即非渐近）且非参数的敏感性分析克服了这些限制，用于$I \times J$或$I \times J \times K$列联表中的未测量混杂因素，能够同时处理非二元处理和非二元结果。 具体来说，我们扩展了Rosenbaum的通用偏倚敏感性模型，并开发了一种通用方法来计算任何置换不变检验的精确最坏情况零分布，这包括卡方检验以及许多基于似然或评分的关联检验。 我们还提出了针对置换不变检验子族的特殊结果，这些结果导致更高效的最坏情况零分布计算。 最后，我们表明，基于利用所有处理和结果水平的检验的敏感性分析通常比将分类变量二分化的“天真”方法具有更高的功效。 我们最后使用《早期儿童纵向研究，1998-1999年幼儿园班级》的数据重新分析了学前护理对数学成就的影响。 一个R包sensitivityIxJ实现了所提出的方法。 显示英文摘要

摘要： In observational studies, contingency tables provide a simple and intuitive approach to study associations between categorical variables. However, any test of association in contingency tables may be biased due to unmeasured confounders. Existing sensitivity analyses that assess the impact of unmeasured confounding on association tests typically assume a binary treatment variable or impose strong parametric assumptions on the non-binary treatment variable. We overcome these limitations with an exact (i.e., non-asymptotic) and nonparametric sensitivity analysis for unmeasured confounding in $I \times J$ or $I \times J \times K$ contingency tables, accommodating both non-binary treatment and non-binary outcome. Specifically, we extend Rosenbaum's sensitivity model for generic bias and develop a general method to calculate the exact worst-case null distribution for any permutation-invariant test, which includes the chi-square test and many likelihood-based or score-based tests of association. We also propose specialized results for sub-families of permutation-invariant tests that lead to more efficient computation of the worst-case null distribution. Finally, we show that sensitivity analyses based on tests that utilize all treatment and outcome levels typically have higher power than "naive" approaches that dichotomize the categorical variables. We conclude with a re-analysis of the effect of pre-kindergarten care on math achievement using data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999. An R package, sensitivityIxJ, implements the proposed methods.